2009 First International Conference on Future Information Networks

Research on Fault-Tolerant Target DetectionAlgorithm in Wireless Sensor Networks

ZHANG Xi-hong GAO Yan-yan ZHOU Shun YIN Cheng-haoDepartment of Computer Engineering

Ordnance Engineering CollegeShijiazhuang China

yang I23sn@163.com. benbengy@126 .com. hbxlzs@sina.com, yinchh_3126@tom.com

II. THREE ALGORITHMS

Figure I. Mainframeof the network

3) Determine Junior nodes: set the threshold ()\ ' a sensor Si

is a Junior sensor if R, > ()\ .

Primary node sActive ba se node sSleeping base nodes

•o

In this paper, we assume that 1000 sensors are deployeduniformly in the detection field, and they first compute theirphysical position through either GPS or some other techniques.Here the three algorithms are mainly focused on fault-toleranceand target detection. The main process is shown as Fig.2.

For the time being, we neglect the communication errors inthe delivers process.

Algorithm I for Target Region Detection:

1) obtain signal measurements: set the threshold AL and AH '

obtain signal measurements {R;, R~, ,R~} form all

sensors in A'(Si) if AL <Ri< AH ;

2) compute the median Mi : M, = { R; ,R~, ,R~}/ n ,

M, is the estimated reading R, at location Si;

Keywords- Target Detection; Fault-Tolerant; Wireless SensorNetwork

I. INTRODUCTION

In the last few years, Wireless sensor networks have beenthe focus of considerable research for both military and civilapplications in which target detection with more efficient andcorrect method is one of the important issues. Sensors aregenerally constrained in energy supply and the quality ofoverall performance. Efficient algorithms and fault-tolerance ofthe network are both crucial to extend the life of the system andimprove the correctness of target detection. Current methodssolve this problem are usually partial or unavailable, ourmethod is designed to provide an optimal solution. The mainframe ofthe network is shown as Fig.I.

Abstract-We have designed an efficient method for Fault­Tolerant Target Detection in wireless Sensor Networks. Withwhich the whole network consists of two main kinds of wirelesssensors--primary sensors and base ones. Small primarysensors are usually cheap and there is a large number of this kindof sensors distributed in the area. The base sensors have a strongcomputational capability but may be more expensive and bigger.They can be divided into two kinds of appearances-sleepingand active. The active ones cover the detection area completelywith little Redundancy. The signal strength a primary sensordetects depends on other primary sensors in their fixed radiorange. Primary sensors locally communicate and compute withothers at the first step and filter out junior sensors based onsignal strength. By use the similar algorithm, they can also filterout senior sensors at the second step which communicate with thebase sensors directly. The base sensor computes the final resultby data aggregation. We reduce the importance of each singlesensor node in order to lower its destructiveness once it is failed.In other words, whether a real target exists or not must be jointlydetermined by neighboring sensors at the same time. We considerdelegating one senior sensor to communicate with the base nodefor each target and compute the position of the target locally inorder to decrease the communication overhead.

978-1-4244-5160-9/09/$26.00 ©2009 IEEE

Basenodes

Figure 2. Main process

Algorithm I is used to detect the presence of targets. The

threshold AL and AH are predefined according to experience,

and the threshold 81 is predefmed based on the quality of

sensors and the actual circumstance, it is the first level to filterout the sensors which can detect the presence of the targets.However it doesn't tell us how many targets exist and wherethey are and shifting the task of target localization to the basestation by sending the measurements of all sensors in the targetregion is too expensive in terms of energy consumption andunnecessary . Therefore we consider one Senior sensor tocommunicate with the base station for each target and computethe position of target locally.

The detection results are shown as follows, in which x-ysurface showed us the deployment of the wireless sensors andthe z label showed us the signal strength of each sensor gets.

Algorithm 2for Target Localization:

1. signal process:

I) Obtain estimated signal strength {R:, R~, ,Rnfrom all Junior nodes in Af(s;) if Si is an Junior sensor;

2) Compute detection coefficient: compute k / n as the

detection coefficient, and set the threshold 82 ,if k / n>

82 then go on else drop the data and break;

3) Determine Senior nodes: an Junior sensor is filtered to bea Senior sensor if its estimated signal strength is a local

maxima among all Junior sensors in Af(Si) , R; =max

{R:,R~, ......,Rn ;4) Filter the Junior nodes in Af(Si) to get a subset

A~Si) ={S: 'S~, .......,Sn ' and estimate the location

of a possible target by the geometric center of the subset.Prepare to report the estimated coordinates and detectioncoefficient to the base station.

Filter method: set 83 to be the threshold that mainly

characterizes the target size.For (i=l; i<=q; i++)

{ if(Rji) ~ R; -83

)

{ Put Si into the subset {S:, S~, ,S~};}

}

2. Correction coefficient

Note that we can check the dissemination characteristicfunction of signal easily, and compute the anti-functionassigned as L =g(R) , here L denote the distance betweenthe sensor and the target, R denote the signal strength at sensorSi.

The correction coefficient

1g(R)

1 n [ 1 ]-I-n ;=1 g(R)

3. Target localization:Set the coordinate of the detected target Di .

Di =[Di(x),Di(y)]

.....: ..' ··1····..·· \ .

:····':·.:i40 . •-

:JJ ••.•.., ..,; ..•........~

10 '.' ."('"."l,."" ..r ,, ' •." / ' .,';}'t-,~-'':7-o .... ..•'. .- ... ", . .~ : . :..~·.'tL~ :.~;:.>-o-- .5Q

eo 0 .~ 9J

Figure 3. Detection Result before Computing

".::: .!

Figure 4. Detection Result after Computing

Algorithm 3for Target Detection:

I) All senior nodes apply algorithms 1 and 2 for each epoch,

and report the target position estimates to the base station

(subjoin detection coefficient).

2) After collecting raw for T epochs, the base station applies

a clustering algorithm to identify for target, for each group

G =[D:,D~ ,D;, ] with cardinality IGI.3) Compute collection coefficient: compute as the collection

coefficient IGI , and set the threshold 83

,if k x IGI > 83TnT

then go on and report a sign else drop the data and reporta false alarm;

4) Target detection. Ii =[li(X),li (Y) ]

=[D: +D~""""+~~IJ!IGI- k IGI

Target detection report [ Ii, - ,-]..n T

III. PERFORMANCE EVALUAnON

To evaluate the performance of this method, we firstlysuppose that the observation noise is a sequence of independentand identically distributed Gaussian components with zero-

2mean and variance (J' • And the evaluation of the targetdetection algorithm includes two main tasks: evaluating theability of fault-tolerance and evaluating the accuracy of targetdetection.

We present the simulation results with 1000 sensorsdeployed in the square region of n*n (m2).here n is variable sothat we can get different network densities. The result can beshown by follows.

A. The ability offault-tolerance

The ability of fault-tolerance mainly depends on thenumber and the influence of fault sensors. here we set that

f3 =F~2' a =%\ where Ail is the number of all

Junior sensors and F;\ is the number of all fault sensors in the

set of A'(Si) , and An is the number of all sensors and F;2 is

the number of all fault sensors in the subset of A~Si) .

Evaluating the ability of lower the influences of fault sensorsby compare the original data and computed data with theoriesdata. Let C(Ri) D(Ri) and T (Ri) denote the computed data

original data and theories data respectively.

The performance of fault-tolerance is evaluated through

the number standard shown by Fig.5 and the influence standard

shown by Fig.6, defmed as

1.3r------;-;-----;-;-----;r=r::==:==::::::::=il1.2 :::: --+- densil y=20

1.1 •• • ••• ). •••••::C::: :::C:::::: ;: :::::::L::: == ~::::: ~:;o: ,L "",:""".L"""j "",J.. \ ..1...",1 --- -- t----- : : : r- : ', :

::S:: :[ , : : ' : ' : ,. : : : : : : : :: : : : : : : : : ; :/~ i: :: : : : : :l: :: : , : :f:s:'!, : : ~/ ' : : \

0.7 --- -- -- 1-· - ~.. - ;---- ~--~- - - --;------ : ; :0.6 --- -- -- ;----- --- ;--------i- ---- ---1------- -1 -- -- --- -j -- --_._+-._--

us "" ' 1"""'1"""' 1"""'1" "" ":"" " ':"" " 1"" "0.4 --- -- j---- -_ ·_ j--------j- ---- ---j----- -- -i -- ----- -j -- ---·--i-------

004 0,C15 0.00 0.1 0.12 0.14 0 .16 0 .16 0.2th Q falHI rah of ~ I Hm$or~

Figure 5. The number evaluation

d.04 0.00 0.06

Figure 6. The influence evaluation

A high N(F;) and a low I(RJ indicate a good fault­

tolerance.

B. The accuracy oftarget detection

The accuracy of target detection is evaluated through thedistance between the detection result and the real position,

defined as

D(li) ={[h(X)-li(X) J2+[h(y)-li(y)J2}Here we expect a low D(li) .

IV. RESULTS

: '" ---+- density=20

~ / ; : ~\ i ---+- den~it ' '''l05 :./ · .. r ·..r..· ;..'\ : ~de':"t ,-7

· . , , . . .· . , , , , .

::.::J.( ...l..:::::.::::.:.:.:....:.t\ ,].....L/ : : : : ' . :

: : : : : ""- ;· . , , , .L ~

2 r··-+·····+·····i · · · · · · ·; · · · · · · · +-· · · · · i · · · · i ~....

[I] A. Thaeler, M. Ding, and X. Cheng, iTPS: An Improved LocationDiscovery Scheme for Sensor Networks with Long Range Beacons, toappear in Special Issue on Theoretical and Algorithmic Aspects ofSensor, Ad Hoc Wireless, and Peer-to-Peer Networks of Journal ofParallel and Distributed Computing, Fall 2004.

[2] W. Zhang and G. Cao, Optimizing Tree Reconfigurations for MobileTarget Tracking in Sensor Networks, IEEE INFOCOM, Hong KongChina, March 2004.

[I] Y. Zou, Krishnendu Chakrabarty, Target Localization Based on EnergyConsiderations in Distributed Sensor Networks, Proc. of the First IEEEInternational Workshop on Sensor Network Protocols and Applications,II May 2003 Pages:51 - 58.

Figure 7, The distance evaluation.

In this paper, we present fault-tolerant method andalgorithms for stationary target detection in wireless sensornetworks. In this study, data aggregation is done along bothtemporal and spatial dimensions for decreasing the false alarmrate and increasing the target position accuracy. Note that thecommunication overhead of our algorithms is low, in mostcases only one message per target will be sent to the basesensor per epoch in moderately dense sensor networks. Finally

- kthe base sensor reports a detection message such as [Ii ,- ,

n

I~I ],Ii denotes the position of a target, %denotes the

Accurate degree of step one, IG){ denotes the accurate

degree of step two. We can set the minimum threshold %and

IG){ in order to get the optimal detection information even if

it is perfect but partial.

804

, , ,, , ,.. ---_._-.--_._---,--------,-----_. .

0,(6 0,00 0.1 0.12 0,14 0.16 O.1BthQh lc:gUt9 of all Hmcor~

REFERENCES

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